scholarly journals Parametric Narrow-Band InISAR 3D Imaging Based on Compressed Sensing

2020 ◽  
Vol 29 (3) ◽  
pp. 508-514
Author(s):  
Baoping WANG ◽  
Yan ZHANG ◽  
Yang FANG ◽  
Zuxun SONG
Keyword(s):  
Compressed Sensing ◽  
3D Imaging ◽  
Narrow Band

Narrow-band radar imaging for off-grid spinning targets via compressed sensing

2016 ◽  
Vol 28 (4) ◽  
pp. 1167-1181 ◽  
Author(s):  
Chao Sun ◽  
Baoping Wang ◽  
Yang Fang ◽  
Zuxun Song
Keyword(s):  
Compressed Sensing ◽  
Narrow Band ◽  
Radar Imaging ◽  
Spinning Targets

scholarly journals Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

2021 ◽  
Vol 13 (9) ◽  
pp. 1751
Author(s):  
Bokun Tian ◽  
Xiaoling Zhang ◽  
Liang Li ◽  
Ling Pu ◽  
Liming Pu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Computational Efficiency ◽  
3D Imaging ◽  
Likelihood Function ◽  
Marginal Likelihood ◽  
Sampling Rate ◽  
Relevance Vector Machine ◽  
Inverse Matrix ◽  
Singular Matrix ◽  
Scattering Coefficients

Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

Narrow Band Interference Elimination based on Compressed Sensing in UWB Energy Detector

2016 ◽  
Vol 66 (2) ◽  
pp. 168
Author(s):  
Priyanka G. Patil ◽  
Gajanan K. Birajdar
Keyword(s):  
Compressed Sensing ◽  
Power Consumption ◽  
Message Passing ◽  
Narrow Band ◽  
Wide Band ◽  
Energy Detection ◽  
Energy Detector ◽  
Large Signal ◽  
Large Bandwidth ◽  
Interference Elimination

<p>Wireless communication applications with large signal bandwidth are developed tremendously in recent times. Due to large bandwidth the wide band communication causes huge power consumption and signal deterioration after addition of narrow band interference (NBI). The ultra wide band (UWB) energy detector, which is highly robust against NBI signal is presented. Compressed sensing is implemented to reduce the power consumption at the analog to digital converter with approximated message passing reconstruction. In addition to this, digital notch is employed to eliminate the NBI affected measurements from compressed version of the received signal before applying it to the energy detector. To analyze the efficiency of the detector, the energy detection and bit error probability of the detector in the absence of NBI and after mitigating NBI is compared. The simulation results are the evidence of effectiveness of the presented energy detector.</p><p> </p>

scholarly journals Compressed Sensing for THz FMCW Radar 3D Imaging

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shanshan Gu ◽  
Guangrong Xi ◽  
Lingyu Ge ◽  
Zhong Yang ◽  
Yizhi Wang ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
3D Imaging ◽  
Continuous Wave ◽  
Imaging System ◽  
Measurement Data ◽  
Radar Signal ◽  
Data Reconstruction ◽  
Signal Model ◽  
Fmcw Radar ◽  
Range Migration

A terahertz (THz) frequency-modulated continuous wave (FMCW) imaging radar system is developed for high-resolution 3D imaging recently. Aiming at the problems of long data acquisition periods and large sample sizes for the developed imaging system, an algorithm based on compressed sensing is proposed for THz FMCW radar 3D imaging in this paper. Firstly, the FMCW radar signal model is built, and the conventional range migration algorithm is introduced for THz FMCW radar imaging. Then, compressed sensing is extended for THz FMCW radar 3D imaging, and the Newton smooth L0-norm (NSL0) algorithm is presented for sparse measurement data reconstruction. Both simulation and measurement experiments demonstrate the feasibility of reconstructing THz images from measurements even at the sparsity rate of 20%.

scholarly journals Compressed Sensing/Sparse-Recovery Approach for Improved Range Resolution in Narrow-Band Radar

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sandra Costanzo
Keyword(s):  
Compressed Sensing ◽  
Narrow Band ◽  
Mathematical Formulation ◽  
Sparse Recovery ◽  
Range Resolution ◽  
Biological Objects ◽  
Resolution Capability ◽  
Experimental Validations ◽  
Recovery Approach ◽  
Recovery Procedure

A compressed sensing/sparse-recovery procedure is adopted to obtain enhanced range resolution capability from the processing of data acquired with narrow-band SFCW radars. A mathematical formulation for the proposed approach is reported and validity limitations are fully discussed, by demonstrating the ability to identify a great number of targets, up to 20, in the range direction. Both numerical and experimental validations are presented, by assuming also noise conditions. The proposed method can be usefully applied for the accurate detection of parameters with very small variations, such as those involved in the monitoring of soil deformations or biological objects.

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